Heuristic optimization for the resource constrained Project Scheduling Problem: A systematic mapping

Abstract

Context: Heuristic optimization has been of strong focus in the recent modeling of the Resource Constrained Project Scheduling Problem (RCPSP), but lack of evidence exists in systematic assessments. New solution methods arise from random evaluation of existing studies. Objective: The current work conducts a secondary study, aiming to systemize existing primary studies in heuristic optimization techniques applied to solving classes of RCPSPs. Method: The systemizing framework consists of performing a systematic mapping study (SM), following a 3-steped protocol. Results: 371 primary studies have been depicted from the multi-stage search and filtering process, to which inclusion and exclusion criteria have been applied. Results have been visually mapped in several distributions. Conclusions: Specific RCPSP classes have been grounded and therefore a rigorous classification is required before performing a systematic mapping. Focusing on recent developments of the RCPSP (2010-2015, a strong interest has been acknowledged on solution methods incorporating AI techniques in meta-and hyper-heuristic algorithms.